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Article type: Research Article
Authors: Ye, Fei-Feia | Wang, Suhuic | Yang, Long-Haoa | Wang, Ying-Minga; b; *
Affiliations: [a] Decision Sciences Institute, Fuzhou University, Fuzhou, China | [b] Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Fuzhou University, Fuzhou, China | [c] School of Business Administration, Zhejiang University of Finance & Economics, Hangzhou, China
Correspondence: [*] Corresponding author. Ying-Ming Wang, E-mail: msymwang@hotmail.com.
Abstract: Air pollution management is becoming a major topic of political concern, and many studies have devoted to the efficiency measurement of air pollution management. However, several drawbacks must be overcome for better applying efficiency measurement to improve air pollution management, including neglect of the importance of different indicators, non-integrity of indicator information for efficiency measurement, and lack of analyzing regional factors in the efficiency of air pollution management. Accordingly, by utilizing the evidential reasoning (ER) approach with entropy weighting method to propose an ER-based indicator integration and introducing the slacks-based measure (SBM) model with consideration of undesirable outputs and the regression model to propose an SBM-based efficiency analysis, a new air pollution management method, called integrated ER-SBM method, is developed in the present study. In the case study of Chinese 29 provinces, the application procedure and results are provided to illustrate how to apply the integrated ER-SBM method to integrate various air pollution indicators with different importance and further analyze the influence of regional factors, such as technological innovation, regional population density, import-export values, number of industries, and energy resources, on the efficiency of air pollution management. In addition, the policy recommendations targeting the results are concluded as well.
Keywords: Air pollution, indicator integration, efficiency analysis, ER approach, SBM model
DOI: 10.3233/JIFS-191816
Journal: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6833-6848, 2020
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